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arXiv · 2407.03922

POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration

Antoine Legouhy·Ross Callaghan·Hojjat Azadbakht·Hui Zhang
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Abstract

This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT.

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Antoine Legouhy, Ross Callaghan, Hojjat Azadbakht, Hui Zhang. 2024-07-04. POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration. https://doi.org/10.1007/978-3-031-34048-2_47

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